Cell division is an essential feature of growth, development and renewal. Each time a cell divides an exact copy of the genome must be made and each replicated chromosome equally distributed to the progeny cells. If chromosomes are unequally distributed, the cell is said to be aneuploid - the state of having the incorrect number of chromosomes. Aneuploidy is a pathological hallmark of cancerous cells and underlies a variety of birth defects. The cellular machinery to prevent aneuploidy is called the mitotic checkpoint and acts to prevent distribution of the chromosomes until they are attached to the mitotic spindle. Unlike genes that cause hereditable cancers (e.g. Rb, BRCA1, p53 etc.), very few mitotic checkpoint genes have been found mutated in disease. However, the overwhelming evidence of aneuploidy in cancer and birth defects strongly implicates a deficiency in the mitotic checkpoint machinery. Recent observations are now converging on a model whereby genes, particularly in the kinetochore-mediated Mad2 signaling pathway, are not necessarily mutated directly, but that the expression levels of genes and thereby reduced activity of the mitotic checkpoint can result in aneuploidy. Here we propose to quantitatively measure the kinetics of kinetochore signaling in living cells and house these measurements in a predictive in silico model of checkpoint signaling. These measurements are made through the marriage of modern molecular biological techniques with cutting- edge microscopy and spectroscopy-based biophysical instrumentation. By determining the array of signals produced by unattached kinetochores to prevent untimely chromosome segregation and the rates at which these are made we will be able to develop an in silico model of the process. The model will allow us to test the quantitative perturbations of these genes in the signaling pathway. This will permit the generation of new hypotheses and experiments regarding the basic mechanisms of the mitotic checkpoint and begin to understand how subtle defects in this process may underlie disease. Quantitative measurements and in silico modeling will provide a solid foundation upon which to study the checkpoint response of varied cell lines and human tumors. Our sensitivity analyses can identify those proteins in this pathway that would be most sensitive to therapeutic intervention. Ultimately, such models may provide predictive capacity to choose chemotherapeutic/anti-mitotic treatment regimen based on the proteomic signature of a human tumor.